For many years people have speculated that electroencephalographic activity or other electrophysiological measures of brain function might provide a new non-muscular channel for sending messages and commands to the external world, commonly referred to as a brain computer interface (BCI). Over the past 20 years, productive BCI research programs have arisen. Encouraged by an improved understanding of brain functions, by the advent of powerful low-cost computer equipment, and by the growing recognition of the needs and potentials of people with disabilities, these programs concentrate on developing new communication and control technologies.
The most popular brain computer interfaces use electro-encephalographic (EEG) activity recorded from the scalp, or single-neuron activity recorded within the cortex. These activities may be used in a computing environment to control cursor movement, select letters or icons, or may be used to operate neuro-prostheses. Central in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls one or more external devices. The operation of a BCI depends on an effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI that recognizes the commands contained in the input and expresses them in device control. Further detail on BCIs can be found in J. R. Wolpaw, N. B., D. J. McFarland, G. Pfurtscheller, T. M. Vaughan, “Brain-computer interfaces for communication and control” Clinical Neurophysiology, 113 (2002) pages 767 to 791, and T. M. Vaughan, W. J. H., L. J. Trejo, W. Z. Rymer, “Brain-Computer Interface Technology: A Review of the Second International Meeting” IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2003 11(2) pages 94 to 109.
BCIs may be provided to individuals with severe disabilities to improve their quality of life. Brain signals may, for example, be capable of providing enhanced control of devices such as wheelchairs, vehicles, or assistance robots for people with disabilities. As BCI technology improves it will probably expand to serve people with less severe disabilities, partial disabilities, or no medical disabilities at all. For example, BCIs could be used to monitor attention in long-distance drivers or aircraft pilots. BCIs might be used to control robots that function in dangerous or inhospitable situations. In other applications, BCIs might be used to provide additional control in video games or to create neural art and music.
The applications domain for BCIs can be seen as a continuum that runs from a binary switch (one bit, on or off) at one end to analog complex robotics at the other. Current BCIs have maximum information transfer rates of up to 25 bits/min. Achievement of greater speed and accuracy requires improvements in signal acquisition and processing, in translation algorithms, and in user training. The choice of BCI signals is affected by the application. For precise control functions, such as rapid motion of physical devices, the relatively slow changes of some EEG signals may be inadequate, whereas the more rapid dynamics of neuronal spike trains may suffice. However, such a choice forces another trade-off: surface electrodes are convenient and involve little risk whereas implantation of electrodes in the brain is invasive and, therefore, involves more risk.
Prior to a proper usage of a BCI by an individual user, an extensive training is required. First the user has to learn how to modulate their brain activity such that the proper electrophysiological signals are generated, and in addition, the BCI being used has to log many signals of the user and design a model or extract features. However, electroencephalogram (EEG) signals are naturally non-stationary, different from subject to subject and usually very noisy since they are contaminated with various artifacts such as electromyogram (EMG) and electrooculogram (EOG) signals. An electromyogram (EMG) detects the electrical potential generated by muscle cells when these cells contract, and also when the cells are at rest. Electrooculography is a technique for measuring the resting potential of the retina. It can be used to detect eye movements, and the adaptation of the eye to changing light conditions.
Both the signal variability and the noises may considerably distort the performance of an EEG classifier. Therefore, for many BCI systems, a tedious and time-consuming training process is usually needed for learning the specific characteristic of the brain signals; see for example X. Liao, D. Y., C. Li, “Transductive SVM for reducing the training effort in BCI” Journal of Neural Engineering, 2007 4(3), pages 246 to 254. In general the subject experiences the training for a BCI as taking too much time, boring and annoying. In some experiments even the offering of a monetary award to adults did not significantly reduce the training time whereas the offering of candy to children was successful. Especially for disabled or elderly people the training is very much a significant burden.